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Frontiers in Applied Productivity Analysis

Paper Session

Sunday, Jan. 9, 2022 12:15 PM - 2:15 PM (EST)

Hosted By: Agricultural and Applied Economics Association
  • Chair: Rigoberto Lopez, University of Connecticut

Accounting for Endogeneity in Rebound Effects

Subal Kumbhakar
State University of New York
Christopher Parmeter
University of Miami


Rebound effects are a natural outcome in various applied economic settings. For example, in the
study of land use and farm productivity, the rebound effect captures how improvements in
technological change and technical efficiency increase land use. In addition, indirect rebound
effects may also result from the adoption of energy efficient products or policies. However, an
issue that has been ignored to date is the likely endogeneity in these settings. This paper reviews the rebound effect literature and proposes a model that deals with the endogeneity of rebound effects by modeling this effect directly into the technology as opposed to using a conventional two-stage setup in productivity analysis. We show how existing methods have upward biases on the rebound effect and how our model corrects for this bias. Ultimately, our results suggest that the importance of the rebound effects stemming from productivity improvements should not be underestimated and should be modeled properly.

On the Estimation of Cross-Firm Productivity with an Application to FDI

Emir Malikov
University of Nevada
Shunan Zhao
Oakland University


Firms improve their productivity by learning from one another. We develop a novel methodology for the proxy variable structural identification of firm productivity in the presence of learning and cross-firm spillovers. We propose a unified one-step analysis of the technology-transfer effects between peer firms, instead of the popular approach that relies on two steps, whereby one first recovers firm productivity estimates and then tests for spillovers in the second step by regressing these productivity estimates on various peer-group averages, capturing firms' exposure to potential spillovers. In contrast, our methodology is both internally consistent and capable of accommodating cross-sectional peer dependence in firm productivity induced by the spillovers, which facilitates identification of both the productivity and spillover effects simultaneously. Because our methodology can be easily adapted to admit various spillover origins, such as research and development, foreign direct investment or exporting, it is fit to investigate cross-firm productivity spillovers in many contexts. We illustrate the applicability of the model with data from China's electric machinery manufacturing industry, with the particular focus on the effects of inbound FDI on productivity via domestic firms' learning of more advanced/efficient foreign knowledge to which they may gain access directly through their own foreign investors and indirectly through spillovers from their foreign-invested peers.

Do Social Disparities Play a Role in Farm Level Productivity Gaps in United States Agriculture?

Eric Njuki
USDA Economic Research Service
Boris Bravo-Ureta
University of Connecticut
Michee Lachaud
Florida A&M University
Nigel Key
USDA Economic Research Service


In the U.S., considerable variability in technological progress has been found across farmers and locations. This paper focuses on productivity differentials between socially disadvantaged farmers and ranchers (SDFR) and non-SDFR groups. SDFRs account for 41 percent of all farmers and ranchers, comprising Native American, Asians, Blacks or African Americans, Latinx, and women. Yet, the role that social disadvantages might play in productivity has received scant attention in the literature. Research indicates that SDFRs typically receive lower levels of and poorer quality education as well as inferior extension services compared to non-SDFRs. Moreover, racial, ethnic, and gender disparities have changed little over time, leaving the SDFR community at a socioeconomic disadvantage and likely lagging in terms of productivity relative to their non-SDFR counterparts. We rigorously test these propositions using farm-level data for several waves of the Agricultural Resource Management Survey (ARMS). First, we analyze single factor productivity measures (e.g., yields and output per worker), and then define farmer groups using propensity score matching. Next, we apply stochastic production frontier methods, including meta-frontiers, to examine technology and meta-technical efficiency gaps. The results are expected to provide novel and unique insights into the efficiency and productivity gaps associated with social disparities between SDFR and non-SDFR farmers.

The Dimensions of Productivity Change in the U.S. Food Industries

Rigoberto Lopez
University of Connecticut
Jordi Jaumandreu
Boston University


This paper explores the evolution of productivity and markups in the U.S. food manufacturing
industries over time and provides some preliminary explanations for the increasing gaps between
revenues and variable costs. We first examine the evolution of labor cost shares and the ratios of revenues to costs using U.S. Census data from the NBER-CES database for 55 food and
beverage industries at the 6-digit NAICS level. We find that estimated labor cost shares are
decreasing, suggesting increasing labor efficiency, but the ratios of revenues to costs are
increasing. We then estimate a Hicks-neutral translog production function with unobserved
productivity shocks. We confirm both the fall of labor revenue shares (something that has
attracted the interest of macroeconomists in explaining increasing income inequality) and the
increase in the ratios of revenues over variable costs (showing gross markups) over time, the
latter being consistent with recent findings for the entire U.S. manufacturing sector. In addition, we find that the decline in labor shares is persistent. We then test for alternative explanations for these trends. We conclude that accounting for changes in relative prices, allowing for laboraugmenting productivity growth, and addressing the possible omission of “new” or “missing” inputs, can explain many of these trends.
JEL Classifications
  • Z0 - General